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Archive of posts filed under the Causal Inference category.

Piranhas in the rain: Why instrumental variables are not as clean as you might have thought

Woke up in my clothes again this morning I don’t know exactly where I am And I should heed my doctor’s warning He does the best with me he can He claims I suffer from delusion But I’m so confident I’m sane It can’t be a statistical illusion So how can you explain Piranhas in […]

Reference for the claim that you need 16 times as much data to estimate interactions as to estimate main effects

Ian Shrier writes: I read your post on the power of interactions a long time ago and couldn’t remember where I saw it. I just came across it again by chance. Have you ever published this in a journal? The concept comes up often enough and some readers who don’t have methodology expertise feel more […]

Some wrong lessons people will learn from the president’s illness, hospitalization, and expected recovery

Jonathan Falk writes about the president’s illness: I [Falk] would think it provides a focused opportunity to make a few salient statistical education points. First, a 6 percent mortality rate (among old people with comorbidities) is really bad, but any single selected person is really quite unlikely to die, or even be really sick. Same […]

Randomized but unblinded experiment on vitamin D as a coronavirus treatment. Let’s talk about what comes next. (Hint: it involves multilevel models.)

Under the heading, “Here we go again,” Dale Lehman writes: If you want to blog on the continuing theme – try this (it’s from Marginal Revolution, the citation): Vitamin D Can Likely End the COVID-19 Pandemic What is striking is the analysis by the Rootclaim group – repeated reliance on p values as […]

A question of experimental design (more precisely, design of data collection)

An economist colleague writes in with a question: What is your instinct on the following. Consider at each time t, 1999 through 2019, there is a probability P_t for some event (e.g., it rains on a given day that year). Assume that P_t = P_1999 + (t-1999)A. So P_t has a linear time trend. What […]

Update on social science debate about measurement of discrimination

Dean Knox writes: Following up on our earlier conversation, we write to share a new, detailed examination of the article, Deconstructing Claims of Post-Treatment Bias in Observational Studies of Discrimination, by Johann Gaebler, William Cai, Guillaume Basse, Ravi Shroff, Sharad Goel, and Jennifer Hill (GCBSGH). Here’s our new paper, Using Data Contaminated by Post-Treatment Selection?, […]

Facemasks in Germany

August Torngren Wartin pointed us to this article, “Unmasked! The effect of face masks on the spread of COVID-19,” by Timo Mitze, Reinhold Kosfeld, Johannes Rode, and Klaus Wälde, and asked what I thought. My reply: I’ve not looked at it in detail but it seems reasonable. I’m sharing this for a few reasons. First, […]

This is your chance to comment on the U.S. government’s review of evidence on the effectiveness of home visiting. Comments are due by 1 Sept.

Emily Sama-Miller writes: The federally sponsored Home Visiting Evidence of Effectiveness (HomVEE) systematic evidence review is seeking public comment on proposed updates to its standards and procedures. HomVEE reviews research literature on home visiting for families with pregnant women and children from birth to kindergarten entry, and its results are used to inform federal funding […]

Somethings do not seem to spread easily – the role of simulation in statistical practice and perhaps theory.

Unlike Covid19, somethings don’t seem to spread easily and the role of simulation in statistical practice (and perhaps theory) may well be one of those. In a recent comment, Andrew provided a link to an interview about the new book Regression and Other Stories by Aki Vehtari, Andrew Gelman, and Jennifer Hill. An interview that covered […]

“100 Stories of Causal Inference”: My talk tomorrow at the Online Causal Inference Seminar

Tues 4 Aug, 11:30am on zoom: 100 Stories of Causal Inference In social science we learn from stories. The best stories are anomalous and immutable. We shall briefly discuss the theory of stories, the paradoxical nature of how we learn from them, and how this relates to forward and reverse causal inference. Then we will […]

“The Taboo Against Explicit Causal Inference in Nonexperimental Psychology”

Kevin Lewis points us to this article by Michael Grosz, Julia Rohrer, and Felix Thoemmes, who write: Causal inference is a central goal of research. However, most psychologists refrain from explicitly addressing causal research questions and avoid drawing causal inference on the basis of nonexperimental evidence. We argue that this taboo against causal inference in […]

BMJ update: authors reply to our concerns (but I’m not persuaded)

Last week we discussed an article in the British Medical Journal that seemed seriously flawed to me, based on evidence such as the above graph. At the suggestion of Elizabeth Loder, I submitted a comment to the paper on the BMJ website. Here’s what I wrote: I am concerned that the model does not fit […]

The importance of descriptive social science and its relation to causal inference and substantive theories

Here’s the abstract to a recent paper, Escaping Malthus: Economic Growth and Fertility Change in the Developing World, by Shoumitro Chatterjee and Tom Vogl: Following mid-twentieth century predictions of Malthusian catastrophe, fertility in the developing world more than halved, while living standards more than doubled. We analyze how fertility change related to economic growth during […]

Would we be better off if randomized clinical trials had never been born?

This came up in discussion the other day. In statistics and medicine, we’re generally told to rely when possible on the statistically significance (or lack of statistical significance) of results from randomized trials. But, as we know, statistical significance has all sorts of problems, most notably that it ignores questions of cost and benefit, and […]

Please socially distance me from this regression model!

A biostatistician writes: The BMJ just published a paper using regression discontinuity to estimate the effect of social distancing. But they have terrible models. As I am from Canada, I had particular interest in the model for Canada, which is on their supplemental material, page 84 [reproduced above]. I could not believe this was published. […]

Association Between Universal Curve Fitting in a Health Care Journal and Journal Acceptance Among Health Care Researchers

Matt Folz points us to this recent JAMA article that features this amazing graph: Beautiful. Just beautiful. I say this ironically.

Further debate over mindset interventions

Warne Following up on this post, “Study finds ‘Growth Mindset’ intervention taking less than an hour raises grades for ninth graders,” commenter D points us to this post by Russell Warne that’s critical of research on growth mindset. Here’s Warne: Do you believe that how hard you work to learn something is more important than […]

“To Change the World, Behavioral Intervention Research Will Need to Get Serious About Heterogeneity”

Beth Tipton, Chris Bryan, and David Yeager write: The increasing influence of behavioral science in policy has been a hallmark of the past decade, but so has a crisis of confidence in the replicability of behavioral science findings. In this essay, we describe a nascent paradigm shift in behavioral intervention research—a heterogeneity revolution—that we believe […]

Adjusting for Type M error

Erik Drysdale discusses and gives some formulas, demonstrating on an example that will be familiar to regular readers of this blog.

Coronavirus jailbreak

Emma Pierson writes: My two sisters and I, with my friend Jacob Steinhardt, spent the last several days looking at the statistical methodology in a paper which has achieved a lot of press – Incarceration and Its Disseminations: COVID-19 Pandemic Lessons From Chicago’s Cook County Jail (results in supplement), published in Health Affairs. (Here’s the […]